Abstract
Multi-level multi-scale resource selection models using machine learning were compared and contrasted for generating predictive maps of jaguar habitat (Panthera onca) in the Brazilian Pantanal. Multiple spatial scales and temporal movement levels were run within several analytical modeling frameworks for comparison. Included in the analysis were multi-scale raster grains (30 m, 90 m, 180 m, 360 m, 720 m, 1440 m) and GPS collaring temporal movement levels (point, path, and step). Various analytical methods were used for comparison of models that could accommodate data structural levels (group, individual, case-control). Models compared included conditional logistic regression, generalized additive modeling (GAM), and classification regression trees, such as random forests (RF) and gradient boosted regression tree (GBM). The goals of the study were to discuss the potential and limitations for machine learning methods using GPS collaring data to produce predictive habitat suitability mapping using the various scales and levels available. Results indicated that choosing the appropriate temporal level and raster scale improved model outputs. Overall, larger level analytical modeling frameworks and those that used multi-scale raster grains showed the best model evaluation with the inherent condition that they predict a broader scale and subset of data. The identification of the appropriate spatial scale, temporal scale and statistical model need careful consideration in predictive mapping efforts.
Highlights
Landscape patterns and processes occur within many spatial and temporal dimensions, and scale is a lens through which to view those dimensions
Predictive maps were generated for the machine learning outputs (RF and GMB) and habitat suitability results were scaled at equal intervals for comparison of the landscape predictions (Figure 2)
Similar to other studies that have sought to compare ML methods to conditional logistic regression [14] [19], this study found that machine learning methods perform better in general
Summary
Landscape patterns and processes occur within many spatial and temporal dimensions, and scale is a lens through which to view those dimensions. Following resource selection modeling frameworks, one of the approaches researchers apply to integrate scales are buffers of various sizes around the data points to average environmental covariates within a given area. This allows one to assess the effective scale at which the environment shapes animal behavior [5]. McGarigal et al (2016) proposed a multi-scale, multi-level modeling framework to consider the various spatial and temporal scales necessary to address spatial dependencies within various levels of selection. By quantifying the patterns and processes that naturally occur at different scales in time and space, we can reach conclusions regarding the key ecological and evolutionary processes that compose landscapes
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